Network-based protein-protein interaction prediction method maps perturbations of cancer interactome.

The perturbations of protein-protein interactions (PPIs) were found to be the main cause of cancer. Previous PPI prediction methods which were trained with non-disease general PPI data were not compatible to map the PPI network in cancer. Therefore, we established a novel cancer specific PPI predict...

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Autores principales:	Jiajun Qiu, Kui Chen, Chunlong Zhong, Sihao Zhu, Xiao Ma
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Genetics QH426-470
Acceso en línea:	https://doaj.org/article/2c912d5576dc4c6bbf83331884549097
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spelling	oai:doaj.org-article:2c912d5576dc4c6bbf833318845490972021-12-02T20:03:26ZNetwork-based protein-protein interaction prediction method maps perturbations of cancer interactome.1553-73901553-740410.1371/journal.pgen.1009869https://doaj.org/article/2c912d5576dc4c6bbf833318845490972021-11-01T00:00:00Zhttps://doi.org/10.1371/journal.pgen.1009869https://doaj.org/toc/1553-7390https://doaj.org/toc/1553-7404The perturbations of protein-protein interactions (PPIs) were found to be the main cause of cancer. Previous PPI prediction methods which were trained with non-disease general PPI data were not compatible to map the PPI network in cancer. Therefore, we established a novel cancer specific PPI prediction method dubbed NECARE, which was based on relational graph convolutional network (R-GCN) with knowledge-based features. It achieved the best performance with a Matthews correlation coefficient (MCC) = 0.84±0.03 and an F1 = 91±2% compared with other methods. With NECARE, we mapped the cancer interactome atlas and revealed that the perturbations of PPIs were enriched on 1362 genes, which were named cancer hub genes. Those genes were found to over-represent with mutations occurring at protein-macromolecules binding interfaces. Furthermore, over 56% of cancer treatment-related genes belonged to hub genes and they were significantly related to the prognosis of 32 types of cancers. Finally, by coimmunoprecipitation, we confirmed that the NECARE prediction method was highly reliable with a 90% accuracy. Overall, we provided the novel network-based cancer protein-protein interaction prediction method and mapped the perturbation of cancer interactome. NECARE is available at: https://github.com/JiajunQiu/NECARE.Jiajun QiuKui ChenChunlong ZhongSihao ZhuXiao MaPublic Library of Science (PLoS)articleGeneticsQH426-470ENPLoS Genetics, Vol 17, Iss 11, p e1009869 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Genetics QH426-470
spellingShingle	Genetics QH426-470 Jiajun Qiu Kui Chen Chunlong Zhong Sihao Zhu Xiao Ma Network-based protein-protein interaction prediction method maps perturbations of cancer interactome.
description	The perturbations of protein-protein interactions (PPIs) were found to be the main cause of cancer. Previous PPI prediction methods which were trained with non-disease general PPI data were not compatible to map the PPI network in cancer. Therefore, we established a novel cancer specific PPI prediction method dubbed NECARE, which was based on relational graph convolutional network (R-GCN) with knowledge-based features. It achieved the best performance with a Matthews correlation coefficient (MCC) = 0.84±0.03 and an F1 = 91±2% compared with other methods. With NECARE, we mapped the cancer interactome atlas and revealed that the perturbations of PPIs were enriched on 1362 genes, which were named cancer hub genes. Those genes were found to over-represent with mutations occurring at protein-macromolecules binding interfaces. Furthermore, over 56% of cancer treatment-related genes belonged to hub genes and they were significantly related to the prognosis of 32 types of cancers. Finally, by coimmunoprecipitation, we confirmed that the NECARE prediction method was highly reliable with a 90% accuracy. Overall, we provided the novel network-based cancer protein-protein interaction prediction method and mapped the perturbation of cancer interactome. NECARE is available at: https://github.com/JiajunQiu/NECARE.
format	article
author	Jiajun Qiu Kui Chen Chunlong Zhong Sihao Zhu Xiao Ma
author_facet	Jiajun Qiu Kui Chen Chunlong Zhong Sihao Zhu Xiao Ma
author_sort	Jiajun Qiu
title	Network-based protein-protein interaction prediction method maps perturbations of cancer interactome.
title_short	Network-based protein-protein interaction prediction method maps perturbations of cancer interactome.
title_full	Network-based protein-protein interaction prediction method maps perturbations of cancer interactome.
title_fullStr	Network-based protein-protein interaction prediction method maps perturbations of cancer interactome.
title_full_unstemmed	Network-based protein-protein interaction prediction method maps perturbations of cancer interactome.
title_sort	network-based protein-protein interaction prediction method maps perturbations of cancer interactome.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/2c912d5576dc4c6bbf83331884549097
work_keys_str_mv	AT jiajunqiu networkbasedproteinproteininteractionpredictionmethodmapsperturbationsofcancerinteractome AT kuichen networkbasedproteinproteininteractionpredictionmethodmapsperturbationsofcancerinteractome AT chunlongzhong networkbasedproteinproteininteractionpredictionmethodmapsperturbationsofcancerinteractome AT sihaozhu networkbasedproteinproteininteractionpredictionmethodmapsperturbationsofcancerinteractome AT xiaoma networkbasedproteinproteininteractionpredictionmethodmapsperturbationsofcancerinteractome
_version_	1718375649819754496

Network-based protein-protein interaction prediction method maps perturbations of cancer interactome.

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